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Valuing data in aircraft maintenance through big data analytics: A probabilistic approach for capacity planning using Bayesian networks

机译：通过大数据分析评估飞机维护中的数据：使用贝叶斯网络进行容量规划的一种概率方法

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摘要

Capacity planning is an important problem faced by aircraft Maintenance, Repair and Overhaul (MRO) organizations given the uncertainty of maintenance workloads. Despite the considerable amount of data generated and stored during the planning process, these have yet to provide a decisive competitive advantage to aircraft MROs. This paper addresses this problem by exploring Bayesian networks (BNs) as a big data and predictive analytics (BDPA) tool to cope with the uncertainty on both scheduled and unscheduled maintenance workloads and to improve the MROs capacity planning decision-making process based on incomplete information. The BNs were developed from a real industrial dataset referring to 372 aircraft maintenance projects of a Portuguese MRO and comprise information variables representing typical information collected during the planning process and hypothesis variables representing the workloads required to be estimated. The benefits of applying BNs as a BDPA tool in aircraft maintenance are demonstrated through examples referring to capacity planning, but also sales planning, using real maintenance data. The BDPA tool based on BNs is generic and can be applied to the maintenance capacity planning process of any MRO, allowing accurate estimations and more informed decisions to be made when compared to current practices, which are based on descriptive statistics of past maintenance workloads.
机译：鉴于维修工作量的不确定性，容量规划是飞机维修，修理和大修（MRO）组织面临的重要问题。尽管在计划过程中生成和存储了大量数据，但这些数据尚未为飞机MRO提供决定性的竞争优势。本文通过探索贝叶斯网络（BN）作为大数据和预测分析（BDPA）工具来解决此问题，以应对计划内和计划外维护工作量的不确定性，并基于不完整信息改进MRO容量规划决策过程。 BN是从一个实际的工业数据集中开发出来的，该数据集涉及葡萄牙MRO的372个飞机维修项目，并且包含代表在计划过程中收集的典型信息的信息变量和代表需要估算的工作量的假设变量。通过示例参考容量规划，以及使用实际维护数据进行销售计划，可以证明在飞机维护中将BN用作BDPA工具的好处。基于BN的BDPA工具是通用的，可以应用于任何MRO的维护能力计划流程，与基于过去维护工作量的描述性统计的当前实践相比，可以进行准确的估算和更明智的决策。

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